title: A framework on surface-based connectivity quantification for the human brain.
abstract: Quantifying the connectivity between arbitrary surface patches in the human brain cortex can be used in studies on brain function and to characterize clinical diseases involving abnormal connectivity. Cortical regions of human brain in their natural forms can be represented in surface formats. In this paper, we present a framework to quantify connectivity using cortical surface segmentation and labeling from structural magnetic resonance images, tractography from diffusion tensor images, and nonlinear inter-subject registration. For a single subject, the connectivity intensity of any point on the cortical surface is set to unity if the point is connected and zero if it is not connected. The connectivity proportion is defined as the ratio of the total connected surface area to the total area of the surface patch. By nonlinearly registering the connectivity data of a group of normal controls into a template space, a population connectivity metric can be defined as either the average connectivity intensity of a cortical point or the average connectivity proportion of a cortical region. In the template space, a connectivity profile and a connectivity histogram of an arbitrary cortical region of interest can then be derived from these connectivity quantification values. Results from the application of these quantification metrics to a population of schizophrenia patients and normal controls are presented, revealing connectivity signatures of specified cortical regions and detecting connectivity abnormalities.
authors.affiliation: Advanced Imaging Research Center, University of Texas Southwestern Medical Center, United States. hao.huang@utsouthwestern.edu
authors.forename: Hao
authors.initials: H
authors.lastname: Huang
authors.forename: Jerry L
authors.initials: JL
authors.lastname: Prince
authors.forename: Virendra
authors.initials: V
authors.lastname: Mishra
authors.forename: Aaron
authors.initials: A
authors.lastname: Carass
authors.forename: Bennett
authors.initials: B
authors.lastname: Landman
authors.forename: Denise C
authors.initials: DC
authors.lastname: Park
authors.forename: Carol
authors.initials: C
authors.lastname: Tamminga
authors.forename: Richard
authors.initials: R
authors.lastname: King
authors.forename: Michael I
authors.initials: MI
authors.lastname: Miller
authors.forename: Peter C M
authors.initials: PC
authors.lastname: van Zijl
authors.forename: Susumu
authors.initials: S
authors.lastname: Mori
mesh_terms: D000328:Adult; D001111:Arcuate Nucleus of Hypothalamus / Q000166:cytology / Q000473:pathology; D001931:Brain Mapping / Q000379:methods / Q000592:standards; D002540:Cerebral Cortex / Q000166:cytology* / Q000473:pathology; D056324:Diffusion Tensor Imaging / Q000379:methods* / Q000592:standards*; D005260:Female; D006801:Humans; D008297:Male; D008959:Models, Neurological; D009415:Nerve Net / Q000166:cytology* / Q000473:pathology; D012559:Schizophrenia / Q000473:pathology*; D013499:Surface Properties